Results 161 to 170 of about 443,564 (206)

POI Recommendation Based on Heterogeneous Network

2020
With the development of wireless networks and positioning technologies, location-based social networks (LBSN) have gained popularity. More and more people share experiences about points of interest (POI) through “check-in” behavior. Mining the check-in data can help people discover the POI they are interested in.
Yan Wen 0002, Jiansong Zhang, Geng Chen 0002, Xin Chen 0079, Ming Chen 0001   +4 more
openaire   +2 more sources

Curriculum Meta-Learning for Next POI Recommendation

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
Next point-of-interest (POI) recommendation is a hot research field where a recent emerging scenario, next POI to search recommendation, has been deployed in many online map services such as Baidu Maps. One of the key issues in this scenario is providing satisfactory recommendation services for cold-start cities with a limited number of user-POI ...
Yudong Chen 0003, Xin Wang 0019, Miao Fan, Jizhou Huang, Shengwen Yang, Wenwu Zhu 0001   +5 more
openaire   +1 more source

A Multi-factor Recommendation Algorithm for POI Recommendation

2018
Point-of-Interest (POI) recommendation is an important service in Location-Based Social Networks (LBSNs). There are several approaches, such as collaborating filtering or content-based filtering, to solving the problem, but the quality of recommendation is low because of lack of personalized influencing factors for each user.
Rong Yang, Xiaofeng Han, Xingzhong Zhang
openaire   +2 more sources

Clustering Users’ POIs Visit Trajectories for Next-POI Recommendation

2018
A novel recommender system that supports tourists in choosing interesting and novel points of interests (POIs) is here presented. It can deal with situations where users’ data is scarce and there is no additional information about users apart from their past POIs visits.
David Massimo, Francesco Ricci 0001
openaire   +2 more sources

Hierarchical POI Attention Model for Successive POI Recommendation

2021
The rapid growth of location-based social networks developed a large number of point-of-interests (POIs). POI recommendation task aims to predict users’ successive POIs, which has attracted more and more research interests recently. POI recommendation is achieved based on POI context, which contains a variety of information, including check-in sequence
openaire   +1 more source

On accurate POI recommendation via transfer learning

Distributed and Parallel Databases, 2020
Point of interest (POI) recommendation is of great value for both service providers and users. However, it is hard due to data scarcity. To this end, in this paper, we propose a transfer learning based deep neural model, which fuses valueable cross-domain knowledge to achieve more accurate POI recommendation.
Hao Zhang, Siyi Wei, Xiaojiao Hu, Ying Li, Jiajie Xu 0001   +4 more
openaire   +1 more source

Deep Transfer Learning for Successive POI Recommendation

2021
Personalized POI recommendation attracts more and more attention from both industrial and research fields. Due to data collection mechanism, it is common to see data collection with the unbalanced spatial distribution. For example, some cities may release check-ins for multiple years while others only release a few days of data.
Haining Tan, Di Yao 0001, Jingping Bi
openaire   +1 more source

Capturing Geographical Influence in POI Recommendations

2013
Point-of-Interest POI recommendation is a significant service for location-based social networks LBSNs. It recommends new places such as clubs, restaurants, and coffee bars to users. Whether recommended locations meet users' interests depends on three factors: user preference, social influence, and geographical influence.
Shenglin Zhao, Irwin King, Michael R. Lyu   +2 more
openaire   +2 more sources

POI recommendation with geographical and multi-tag influences

2016 International Conference on Behavioral, Economic and Socio-cultural Computing (BESC), 2016
In this paper, we propose a method for point of interest (POI) recommendation by extracting the multi-tag influence and modeling the geographical influence. First of all, we extract a user-tag matrix from the initial user-POI rating matrix by analyzing the relations between POI and the related bag of tags.
Zhiyuan Zhang 0003, Yun Liu 0001, Haiqiang Chen, Qing Liu 0001   +3 more
openaire   +2 more sources

An ELM-Based Ensemble Strategy for POI Recommendation

2019
The prosperity of Location-Based Social Networks (LBSNs) facilitates a promising focus on personalized POI recommendation. The check-in activity is a multiple criteria decision making process. In this paper, we devise a novel personalized POI recommendation system called Multiple Criteria Ensemble Recommendation System (MCERS) to integrate the effect ...
Xue He, Tiancheng Zhang 0001, Hengyu Liu 0001, Ge Yu 0001   +3 more
openaire   +1 more source